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TZID:Asia/Seoul
X-LIC-LOCATION:Asia/Seoul
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DTSTART:18871231T000000
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BEGIN:VEVENT
DTSTAMP:20230103T035307Z
LOCATION:Auditorium\, Level 5\, West Wing
DTSTART;TZID=Asia/Seoul:20221206T100000
DTEND;TZID=Asia/Seoul:20221206T120000
UID:siggraphasia_SIGGRAPH Asia 2022_sess153_papers_171@linklings.com
SUMMARY:Learning to Relight Portrait Images via a Virtual Light Stage and 
 Synthetic-to-Real Adaptation
DESCRIPTION:Technical Papers\n\nLearning to Relight Portrait Images via a 
 Virtual Light Stage and Synthetic-to-Real Adaptation\n\nYeh, Nagano, Khami
 s, Kautz, Liu...\n\nGiven a portrait image of a person and an environment 
 map of the target lighting, portrait relighting aims to re-illuminate the 
 person in the image as if the person appeared in an environment with the t
 arget lighting. To achieve high-quality results, recent methods rely on de
 ep learning. An effective approach is to supervise the training of deep ne
 ural networks with a high-fidelity dataset of desired input--output pairs,
  captured with a light stage. However, acquiring such data requires an exp
 ensive special capture rig and time-consuming efforts, limiting access to 
 only a few resourceful laboratories. To address the limitation, we propose
  a new approach that can perform on par with the state-of-the-art (SOTA) r
 elighting methods without requiring a light stage. Our approach is based o
 n the realization that a successful relighting of a portrait image depends
  on two conditions. First, the physics of light transport has to be correc
 t. Second, the output has to be photorealistic. To meet the first conditio
 n, we propose to train the relighting network with training data generated
  by a virtual light stage that performs physically-based rendering on vari
 ous 3D synthetic humans under different environment maps. To meet the seco
 nd condition, we develop a novel synthetic-to-real approach to bring photo
 realism to the relighting network output. In addition to achieving SOTA re
 sults, our approach offers several advantages over the prior methods, incl
 uding controllable glares on glasses and more temporally-consistent result
 s for relighting videos.\n\nRegistration Category: FULL ACCESS, EXPERIENCE
  PLUS ACCESS, EXPERIENCE ACCESS, TRADE EXHIBITOR\n\nLanguage: ENGLISH\n\nF
 ormat: IN-PERSON
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_171&sess=sess15
 3
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